MCP Server for Enterprise Systems

The practical limit on AI coding agent usefulness in an enterprise context is access to internal context. An agent that can read public documentation and reason about code can take you a long way, but it can't tell you that a deployment is blocked because of a change freeze in ITSM, or that the asset it's querying is marked as end-of-life in the CMDB, or that there's an open incident affecting the service it's trying to modify. That context lives in internal systems, and getting it into the agent's context window requires a protocol bridge.

MCP (Model Context Protocol) is that bridge. The server exposes resources — Jira issues, Confluence pages, JSM Assets records — through a standard interface that AI agents can query using tool calls. The design is deliberately read-only: the server has no write paths. An agent can read a ticket, but it cannot close it; it can read asset records, but it cannot modify them. That constraint is security-by-design rather than a limitation — it keeps the agent in an advisory role while the human retains execution authority.

OAuth 2.0 token-based authentication with per-tool scope constraints means each type of resource access requires explicit authorisation. Prompt injection defences — schema validation on inputs, output sanitisation before returning data to the agent — address the risk that a maliciously crafted document in Confluence could attempt to redirect the agent's behaviour. The server is the right pattern for AI-augmented enterprise workflows: give the agent context it needs, keep it out of systems it shouldn't touch.